A novel optical fibre analysis system for particle accelerators

Lead Research Organisation: University of Liverpool
Department Name: Physics

Abstract

Some processes occurring within particle accelerators, such as stray particles escaping from the beam (beam loss), can damage or irradiate the machine. This can impede or prevent the function of the accelerator, and the irradiation delays machine maintenance as any residual radioactivity it creates must decay to safe levels before work can begin.

Particle accelerator user facilities commonly aim to minimise machine downtime to offer maximum availability of beam time to facility users. Since a damaged machine requires downtime for repairs [1], and high irradiation levels extend the machine downtime, prevention of both damage and irradiation is important to this goal. Typically, this is performed through monitoring of beam losses to ensure its occurrences are kept to safe levels.

Localised detectors such as ionisation chambers (ICs) are commonly used to monitor beam losses. However, they can each individually only monitor a small area; hence it is unfeasible to precisely determine locations of beam loss using ICs. [2]

RF accelerating cavities can sometimes undergo sparking within them (RF breakdown). This damages the cavity surface and also the klystron amplifiers as the incoming RF power is reflected [3], [4]. Conventional RF breakdown detectors follow the increase in current or pressure that accompanies a breakdown [5]. Since these detectors respond after the onset of breakdown, the available damage mitigation methods are limited to reactive measures.

A solution is offered through optical fibre-based detectors to detect beam losses and RF breakdowns through the showers of secondary particles these events produce. Passage of these relativistic particles through an optical fibre produces Cherenkov light, which propagates to photodetectors. A time-of-flight calculation is then used to determine the loss location [6]. These detectors are non-invasive which can offer, continuous coverage of a machine; simultaneous monitoring of both beam losses and RF breakdowns; a fast response time (on the order of a few nanoseconds); and event location resolution on the order of a few cm (for the current version of the detector system). Unlike ICs, optical fibres are insensitive to magnetic fields and X-rays. This allows fibres to be positioned in areas where many other detectors cannot, such as inside magnetic structures like undulators [7].

Optical fibre technology has been successful on a variety of accelerators, where it has demonstrated sufficient capability to protect individual accelerator sections [8, 9] or even entire accelerators [10]; in addition to successfully monitoring dark current and breakdowns in RF cavities [4], [11]. This project will develop and benchmark the new version of the optical fibre detector system and demonstrate its capabilities for beam loss and RF breakdown resolution as a machine protection system for energy recovery linear accelerators (ERLs).

ERLs are a type of novel accelerator that seek to decrease accelerator energy demands by extracting energy from its used, high-energy beams and subsequently transferring it to fresh, low-energy beams. Both used and fresh particle bunches (sub-segments of beams) often circulate simultaneously within the ERL. Contemporary examples include CBETA at Cornell University [12], and the LHeC collider proposed by CERN [13]. Key challenges for this application include distinguishing beam loss signals from individual bunches of fast-repeating beams, such as those typically produced in ERLs; and discriminating between the energies of these bunches to gauge the progress of each bunch through the ERL.

This project also aims to introduce machine learning to the oBLM system [3], [14], for the prediction and prevention of machine damaging events. This allows for the implementation of proactive rather than reactive intervention methods. Testing will be carried out at various facilities, such as CLARA (Daresbury Laboratory), CLEAR (CERN), and CBETA (Cornell).

People

ORCID iD

Angus Jones (Student)

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/X000540/1 01/10/2022 31/03/2026
2751225 Studentship ST/X000540/1 01/10/2022 31/03/2026 Angus Jones